Removing noise from a set of data may require a priori knowledge of the noise distribution. Various techniques of noise removal yield good performance if the set of data obeys certain conditions consistent with technique assumptions, such as noise statistics or, in the case of image processing, the type of patterns contained in the image. However, if the assumptions are not met, these techniques can give rise to artifacts or losses in data.